




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase							:						Optional[Any]					       =  logging.get_logger(__name__)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[Any]	):


      '''simple docstring'''



      lowerCamelCase_							=       'huggingface/label-files'
      lowerCamelCase_							=       'imagenet-1k-id2label.json'
      lowerCamelCase_							=       json.load(open(hf_hub_download(lowercase      ,				lowercase      ,				repo_type='dataset'	)      ,				'r'	)	)
      lowerCamelCase_							=       {int(lowercase	): v for k, v in idalabel.items()}
      lowerCamelCase_							=       {v: k for k, v in idalabel.items()}

      lowerCamelCase_							=       'std_conv' if 'bit' in model_name else False

      # note that when using BiT as backbone for ViT-hybrid checkpoints,
      # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
      # config.conv_layer = "std_conv_same"
      lowerCamelCase_							=       BitConfig(
          conv_layer=lowercase      ,				num_labels=10_00      ,				idalabel=lowercase      ,				labelaid=lowercase      ,				)

      return config

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Any	):


      '''simple docstring'''



      if "stem.conv" in name:
            lowerCamelCase_							=       name.replace('stem.conv'      ,				'bit.embedder.convolution'	)
      if "blocks" in name:
            lowerCamelCase_							=       name.replace('blocks'      ,				'layers'	)
      if "head.fc" in name:
            lowerCamelCase_							=       name.replace('head.fc'      ,				'classifier.1'	)
      if name.startswith('norm'	):
            lowerCamelCase_							=       'bit.' + name
      if "bit" not in name and "classifier" not in name:
            lowerCamelCase_							=       'bit.encoder.' + name

      return name

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCamelCase_							=       Image.open(requests.get(lowercase      ,				stream=lowercase	).raw	)
      return im

@torch.no_grad()
def      _SCREAMING_SNAKE_CASE     (				lowercase      :			str      ,				lowercase      :			int      ,				lowercase      :			Union[str, Any]=False	):


      '''simple docstring'''



      lowerCamelCase_							=       get_config(lowercase	)

      # load original model from timm
      lowerCamelCase_							=       create_model(lowercase      ,				pretrained=lowercase	)
      timm_model.eval()

      # load state_dict of original model
      lowerCamelCase_							=       timm_model.state_dict()
      for key in state_dict.copy().keys():
            lowerCamelCase_							=       state_dict.pop(lowercase	)
            lowerCamelCase_							=       val.squeeze() if 'head' in key else val

      # load HuggingFace model
      lowerCamelCase_							=       BitForImageClassification(lowercase	)
      model.eval()
      model.load_state_dict(lowercase	)

      # create image processor
      lowerCamelCase_							=       create_transform(**resolve_data_config({}      ,				model=lowercase	)	)
      lowerCamelCase_							=       transform.transforms

      lowerCamelCase_							=       {
          'bilinear': PILImageResampling.BILINEAR,
          'bicubic': PILImageResampling.BICUBIC,
          'nearest': PILImageResampling.NEAREST,
      }

      lowerCamelCase_							=       BitImageProcessor(
          do_resize=lowercase      ,				size={'shortest_edge': timm_transforms[0].size}      ,				resample=pillow_resamplings[timm_transforms[0].interpolation.value]      ,				do_center_crop=lowercase      ,				crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}      ,				do_normalize=lowercase      ,				image_mean=timm_transforms[-1].mean.tolist()      ,				image_std=timm_transforms[-1].std.tolist()      ,				)

      lowerCamelCase_							=       prepare_img()
      lowerCamelCase_							=       transform(lowercase	).unsqueeze(0	)
      lowerCamelCase_							=       processor(lowercase      ,				return_tensors='pt'	).pixel_values

      # verify pixel values
      assert torch.allclose(lowercase      ,				lowercase	)

      # verify logits
      with torch.no_grad():
            lowerCamelCase_							=       model(lowercase	)
            lowerCamelCase_							=       outputs.logits

      print('Logits:'      ,				logits[0, :3]	)
      print('Predicted class:'      ,				model.config.idalabel[logits.argmax(-1	).item()]	)
      lowerCamelCase_							=       timm_model(lowercase	)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(lowercase      ,				outputs.logits      ,				atol=1e-3	)
      print('Looks ok!'	)

      if pytorch_dump_folder_path is not None:
            Path(lowercase	).mkdir(exist_ok=lowercase	)
            print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}"""	)
            model.save_pretrained(lowercase	)
            processor.save_pretrained(lowercase	)

      if push_to_hub:
            print(f"""Pushing model {model_name} and processor to the hub"""	)
            model.push_to_hub(f"""ybelkada/{model_name}"""	)
            processor.push_to_hub(f"""ybelkada/{model_name}"""	)


if __name__ == "__main__":
 lowerCamelCase							:						Optional[Any]					       =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="resnetv2_50x1_bitm",
     type=str,
     help="Name of the BiT timm model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub",
     action="store_true",
     help="Whether to push the model to the hub.",
 )

 lowerCamelCase							:						Optional[int]					       =  parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowercase    :   List[Any]						=			'Run commands across TPU VMs for initial setup before running `accelerate launch`.'




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any]=None)   ->       List[str]:






							'''simple docstring'''





							if subparsers is not None:
														__UpperCamelCase      :  int       =   subparsers.add_parser("tpu-config"		,    description=_description)
							else:
														__UpperCamelCase      :  Dict       =   argparse.ArgumentParser("Accelerate tpu-config command"		,    description=_description)
							# Core arguments
							__UpperCamelCase      :  Optional[Any]       =   parser.add_argument_group(
							    "Config Arguments"		,    "Arguments that can be configured through `accelerate config`.")
							config_args.add_argument(
							    "--config_file"		,    type=_lowerCamelCase		,    default=_lowerCamelCase		,    help="Path to the config file to use for accelerate."		,    )
							config_args.add_argument(
							    "--tpu_name"		,    default=_lowerCamelCase		,    help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."		,    )
							config_args.add_argument(
							    "--tpu_zone"		,    default=_lowerCamelCase		,    help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."		,    )
							__UpperCamelCase      :  Tuple       =   parser.add_argument_group("TPU Arguments"		,    "Arguments for options ran inside the TPU.")
							pod_args.add_argument(
							    "--use_alpha"		,    action="store_true"		,    help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."		,    )
							pod_args.add_argument(
							    "--command_file"		,    default=_lowerCamelCase		,    help="The path to the file containing the commands to run on the pod on startup."		,    )
							pod_args.add_argument(
							    "--command"		,    action="append"		,    nargs="+"		,    help="A command to run on the pod. Can be passed multiple times."		,    )
							pod_args.add_argument(
							    "--install_accelerate"		,    action="store_true"		,    help="Whether to install accelerate on the pod. Defaults to False."		,    )
							pod_args.add_argument(
							    "--accelerate_version"		,    default="latest"		,    help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."		,    )
							pod_args.add_argument(
							    "--debug"		,    action="store_true"		,    help="If set, will print the command that would be run instead of running it.")

							if subparsers is not None:
														parser.set_defaults(func=_lowerCamelCase)
							return parser




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(_lowerCamelCase):
														__UpperCamelCase      :  Dict       =   load_config_from_file(args.config_file)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					__UpperCamelCase      :  List[Any]       =   defaults.command_file
														if not args.command and defaults.commands is not None:
																					__UpperCamelCase      :  int       =   defaults.commands
														if not args.tpu_name:
																					__UpperCamelCase      :  int       =   defaults.tpu_name
														if not args.tpu_zone:
																					__UpperCamelCase      :  Optional[Any]       =   defaults.tpu_zone
							if args.accelerate_version == "dev":
														__UpperCamelCase      :  List[str]       =   "git+https://github.com/huggingface/accelerate.git"
							elif args.accelerate_version == "latest":
														__UpperCamelCase      :  Optional[int]       =   "accelerate -U"
							elif isinstance(parse(args.accelerate_version)		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'accelerate=={args.accelerate_version}'

							if not args.command_file and not args.command:
														raise ValueError("You must specify either a command file or a command to run on the pod.")

							if args.command_file:
														with open(args.command_file		,    "r") as f:
																					__UpperCamelCase      :  List[Any]       =   [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							__UpperCamelCase      :  str       =   ["cd /usr/share"]
							if args.install_accelerate:
														new_cmd += [F'pip install {args.accelerate_version}']
							new_cmd += args.command
							__UpperCamelCase      :  str       =   "; ".join(_lowerCamelCase)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							__UpperCamelCase      :  List[Any]       =   ["gcloud"]
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'Running {" ".join(_lowerCamelCase)}')
														return
							subprocess.run(_lowerCamelCase)
							print("Successfully setup pod.")




def       _SCREAMING_SNAKE_CASE   (						)   ->       List[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[Any]       =   tpu_command_parser()
							__UpperCamelCase      :  Optional[Any]       =   parser.parse_args()

							tpu_command_launcher(_lowerCamelCase)